Image Classification of Tomato Leaf Diseases using Convolutional Neural Network

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Nutchanun Chinpanthana


Tomato is one of the most important cultivated vegetable plants in the world. The continuous expanded production and consumption of tomato has grown quickly. It is considered a mainstay of many economic country. Tomato crops can be damage due to various kinds of diseases that are recently discovery of diagnosis errors or not prevented and controlled timely. The problems faced by farmers are typically unnoticed and lack knowledge in crop production. For developing an early treatment process, the identify infections of plant diseases in a rapid can help to reduce huge economical suffering. In agricultural practices is detect of disease manually on crops which is very complex, time-consuming and more tedious tasks. This paper discussing the technique base on digital image processing, which employs the convolutional neural network deep learning model to classify tomato leaf diseases. The dataset is classified into 10 classes: bacterial leaf spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, cucumber mosaic virus, yellow leaf curl virus and fresh leaf. The approach is composed of four main phases: (1) data preprocessing, (2) generated model convolutional neural network, and (3) model evaluation and (4) deployment. The experimental results indicated that deep learning with convolutional neural network technique has the highest effectiveness in recognizing tomato leaf diseases with the total average accuracy at 87.96% at learning rate 0.001 for 100 epochs.

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How to Cite
N. Chinpanthana, “Image Classification of Tomato Leaf Diseases using Convolutional Neural Network”, JIST, vol. 13, no. 2, pp. 40–49, Dec. 2023.
Academic Article: Information Systems (Detail in Scope of Journal)


Grieneisen, M.L., Aegerter, B.J., Scott Stoddard, C., Zhang, M., “Yield and fruit quality of grafted tomatoes, and their potential for soil fumigant use reduction,” A meta-analysis. Agronomy for Sustainable Development, vol. 38, no. 29, 2018.

Hasan, R.I., Yusuf, S.M., Alzubaidi, L., “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, vol. 9, no. 10,pp. 1–25, 2020.

Thummabenjapone, P. & Phola, S., “A highly potential fungicide to control Stemphylium sp., a causal agent of gray spot of tomato,” In Proceeding The 8th National Plant Protection Conference. pp. 383-391, 2007.

Dookie, M., Ali, O., Ramsubhag, A., and Jayaraman, J., “Flowering gene regulation in tomato plants treated with brown seaweed extracts,” Scientia Horticulturae. vol. 276, 2021.

Durmus, H., Gunes, E. O., and Kirci, M., “Disease detection on the leaves of the tomato plants by using deep learning”, 2017 6th international conference on agro-geoinformatics. Agro-Geoinformatics, pp. 1–5, 2017.

Mortazi, A.; Bagci, U., “Automatically designing CNN architectures for medical image segmentation,” In Proceedings of the International Workshop on Machine Learning in Medical Imaging, Granada, Spain, pp. 98–106, 2018.

Prabira Kumar Sethya, Nalini Kanta Barpandaa, Amiya Kumar Rathb, Santi Kumari Beherab, “Image Processing Techniques for Diagnosing Rice Plant Disease: A Survey,” Procedia Computer Science, 167, pp. 516–530, 2020.

Mohanty SP, Hughes DP, Salath M., “Using deep learning for image-based plant disease detection,” Front Plant Sci, vol. 7. no. 1419. 2016.

The Plant Village dataset:https:// emmarex / plantdisease

Amara J, Bouaziz B, Algergawy A., “A deep learning based approach for banana leaf diseases classification,” International Journal of Creative Research Thoughts, vol 6. Issue 2, pp. 79-88. 2017.

Rangarajan, A.K.; Purushothaman, R.; Ramesh, A. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 2018, 133, 1040–1047.

Sangeetha, R.; Rani, M., “Tomato Leaf Disease Prediction Using Transfer Learning,” In Proceedings of the International Advanced Computing Conference 2020, Panaji, India, 2020.

Too, E.C.; Yujian, L.; Njuki, S.; Yingchun, L., “A comparative study of fine-tuning deep learning models for plant disease identification,” Comput. Electron. Agric., vol. 161, pp. 272–279, 2019.

Agarwal, M.; Gupta, S.K.; Biswas, K.K., “Development of Efficient CNN model for Tomato crop disease identification. Sustain,” Comput. Inform. Syst. vol. 28, pp. 100407–100421, 2020.

M. A. Tanner, and W. H. Wong, “The Calculation of Posterior Distributions By Data Augmentation,” Journal Of The American Statistical Association, vol. 82 no. 398, pp.528–540, 1987.

M. D. Bloice, C. Stocker, and A. Holzinger, “Augmentor: An Image Augmentation Library for Machine Learning,” The Journal of Open Source Software, vol.2. pp. 1-5, 2017.